php-ai / php-ml

src/Classification/Linear/LogisticRegression.php

Spacing +2 added lines, -2 removed lines

@@ -190,7 +190,7 @@ 
                  * The gradient of the cost function to be used with gradient descent:
                  *		∇J(x) = -(y - h(x)) = (h(x) - y)
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 
@@ -225,7 +225,7 @@ 
                  * The gradient of the cost function:
                  *		∇J(x) = -(h(x) - y) . h(x) . (1 - h(x))
                  */
-                $callback = function ($weights, $sample, $y) use ($penalty) {
+                $callback = function($weights, $sample, $y) use ($penalty) {
                     $this->weights = $weights;
                     $hX = $this->output($sample);
 

src/DimensionReduction/KernelPCA.php

Spacing +5 added lines, -5 removed lines

@@ -174,20 +174,20 @@ 
         switch ($this->kernel) {
             case self::KERNEL_LINEAR:
                 // k(x,y) = xT.y
-                return function ($x, $y) {
+                return function($x, $y) {
                     return Matrix::dot($x, $y)[0];
                 };
             case self::KERNEL_RBF:
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance
                 $dist = new Euclidean();
 
-                return function ($x, $y) use ($dist) {
+                return function($x, $y) use ($dist) {
                     return exp(-$this->gamma * $dist->sqDistance($x, $y));
                 };
 
             case self::KERNEL_SIGMOID:
                 // k(x,y)=tanh(γ.xT.y+c0) where c0=1
-                return function ($x, $y) {
+                return function($x, $y) {
                     $res = Matrix::dot($x, $y)[0] + 1.0;
 
                     return tanh($this->gamma * $res);
@@ -197,7 +197,7 @@ 
                 // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance
                 $dist = new Manhattan();
 
-                return function ($x, $y) use ($dist) {
+                return function($x, $y) use ($dist) {
                     return exp(-$this->gamma * $dist->distance($x, $y));
                 };
 
@@ -222,7 +222,7 @@ 
     protected function projectSample(array $pairs): array
     {
         // Normalize eigenvectors by eig = eigVectors / eigValues
-        $func = function ($eigVal, $eigVect) {
+        $func = function($eigVal, $eigVect) {
             $m = new Matrix($eigVect, false);
             $a = $m->divideByScalar($eigVal)->toArray();
 

src/DimensionReduction/LDA.php

Spacing +1 added lines, -1 removed lines

@@ -157,7 +157,7 @@
 
         // Calculate overall mean of the dataset for each column
         $numElements = array_sum($counts);
-        $map = function ($el) use ($numElements) {
+        $map = function($el) use ($numElements) {
             return $el / $numElements;
         };
         $this->overallMean = array_map($map, $overallMean);

src/Clustering/FuzzyCMeans.php

Spacing +1 added lines, -1 removed lines

@@ -143,7 +143,7 @@
                 $total += $val;
             }
 
-            $this->membership[] = array_map(function ($val) use ($total) {
+            $this->membership[] = array_map(function($val) use ($total) {
                 return $val / $total;
             }, $row);
         }

src/FeatureSelection/VarianceThreshold.php

Spacing +1 added lines, -1 removed lines

@@ -37,7 +37,7 @@
 
     public function fit(array $samples, ?array $targets = null): void
     {
-        $this->variances = array_map(function (array $column) {
+        $this->variances = array_map(function(array $column) {
             return Variance::population($column);
         }, Matrix::transposeArray($samples));
 

src/FeatureSelection/ScoringFunction/UnivariateLinearRegression.php

Spacing +1 added lines, -1 removed lines

@@ -52,7 +52,7 @@
 
         $degreesOfFreedom = count($targets) - ($this->center ? 2 : 1);
 
-        return array_map(function (float $correlation) use ($degreesOfFreedom): float {
+        return array_map(function(float $correlation) use ($degreesOfFreedom): float {
             return $correlation ** 2 / (1 - $correlation ** 2) * $degreesOfFreedom;
         }, $correlations);
     }

src/Helper/Optimizer/StochasticGD.php

Spacing +1 added lines, -1 removed lines

@@ -241,7 +241,7 @@
     {
         // Check for early stop: No change larger than threshold (default 1e-5)
         $diff = array_map(
-            function ($w1, $w2) {
+            function($w1, $w2) {
                 return abs($w1 - $w2) > $this->threshold ? 1 : 0;
             },
             $oldTheta,